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Review

2019

Review

2019

From healthcare to criminal justice, artificial intelligence (AI) is increasingly supporting high-consequence human decisions… Expand

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Review

2019

Review

2019

Static robust optimization (RO) is a methodology to solve mathematical optimization problems with uncertain data. The objective… Expand

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Review

2019

Review

2019

We review the Dempster–Shafer (D–S) belief structure and discuss the measures of plausibility and belief and other measures that… Expand

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Review

2019

Review

2019

Abstract Recent findings in operations research and decision theory show that the eigenvectors derived from pairwise comparison… Expand

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Review

2018

Review

2018

Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It… Expand

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Review

2018

Review

2018

Bilevel optimization is defined as a mathematical program, where an optimization problem contains another optimization problem as… Expand

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Highly Cited

2009

Highly Cited

2009

This introduction to decision theory offers comprehensive and accessible discussions of decision-making under ignorance and risk… Expand

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Highly Cited

1995

Highly Cited

1995

The Bayesian revolution in statistics--where statistics is integrated with decision making in areas such as management, public… Expand

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Highly Cited

1995

Highly Cited

1995

In the first part of the paper we consider the problem of dynamically apportioning resources among a set of options in a worst… Expand

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Highly Cited

1972

Highly Cited

1972

General dynamic team decision problems with linear information structures and quadratic payoff functions are studied. The… Expand

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